Feature selection and its combination with data over-sampling for multi-class imbalanced datasets-Reference-Cited by-同舟云学术

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Feature selection and its combination with data over-sampling for multi-class imbalanced datasets

Published:2024-03 Issue: Volume:153 Page:111267
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Tsai Chih-Fong,Chen Kuan-Chen,Lin Wei-Chao^ORCID

Funder

Chang Gung Memorial Hospital, Linkou

Ministry of Science and Technology

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Reference38 articles.

1. To combat multi-class imbalanced problems by means of over-sampling techniques;Abdi;IEEE Trans. Knowl. Data Eng.,2016

2. Resampling imbalanced class and the effectiveness of feature selection methods for heart failure dataset;Al Khaldy;Int. Robot. Autom. J.,2018

3. LoRAS-an oversampling approach for imbalanced datasets;Bej;Mach. Learn.,2021

4. Ensembles for feature selection: a review and future trends;Bolon-Canedo;Inf. Fusion,2019

5. A survey of predictive modeling on imbalanced domains;Branco;ACM Comput. Surv.,2016

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning;Applied Soft Computing;2024-11

2. An adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection;Computers and Electrical Engineering;2024-08

3. Enhancing Fault Diagnosis in Mechanical Systems with Graph Neural Networks Addressing Class Imbalance;Mathematics;2024-07-01

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